Pydantic’s Surprising Behavior: Forcing SQL Queries to Already Populated Fields

If you’re a seasoned Python developer working with Pydantic and SQLAlchemy, you might have stumbled upon a peculiar issue: Pydantic forces SQL queries to already populated fields. You’re not alone! In this article, we’ll delve into the root cause of this behavior, explore its implications, and most importantly, provide concrete solutions to tackle this problem.

What’s the Issue?

To set the stage, let’s consider a simple example. Suppose we have a Pydantic model called `User` with an `id` field that’s populated automatically by the database:

from pydantic import BaseModel
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Session

Base = declarative_base()

class User(Base, BaseModel):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    email = Column(String)

Now, let’s create a new user and save it to the database:

user = User(name='John Doe', email='[email protected]')
session = Session(bind=engine)
session.add(user)
session.commit()

So far, so good. But here’s where things get interesting. Let’s try to update the `name` field using Pydantic’s `create` method:

updated_user = User.update(id=1, name='Jane Doe', email='[email protected]')

You might expect the `update` method to simply update the `name` field and leave the `id` field untouched. However, Pydantic has other plans. It will force the SQL query to update the `id` field to its current value, even though it’s already populated in the database!

Why Does Pydantic Behave This Way?

The reason behind this behavior lies in Pydantic’s design philosophy. By default, Pydantic assumes that all fields in a model are dirty, meaning they need to be updated in the database. This approach ensures that all fields are synchronized between the Pydantic model and the underlying database.

In our example, when we call `User.update`, Pydantic doesn’t know that the `id` field is already populated in the database. It simply treats it as a regular field that needs to be updated. This leads to the unexpected behavior of forcing the SQL query to update the `id` field to its current value.

Solutions to the Problem

Now that we understand the root cause, let’s explore some solutions to tackle this issue.

1. Use SQLAlchemy’s `expire_on_commit=False`

One way to avoid this problem is to set `expire_on_commit=False` when creating the SQLAlchemy session:

session = Session(bind=engine, expire_on_commit=False)

This tells SQLAlchemy to not expire the session after committing changes, which allows us to reuse the same session for subsequent updates. However, this approach has its own set of implications, such as increased memory usage and potential data inconsistencies.

2. Use Pydantic’s `exclude_unset`

A more elegant solution is to use Pydantic’s `exclude_unset` parameter when creating a new instance of the model:

updated_user = User(id=1, name='Jane Doe', email='[email protected]', exclude_unset=True)

This instructs Pydantic to exclude any fields that are not explicitly set in the `update` method, effectively ignoring the `id` field and preventing the unwanted update.

3. Use SQLAlchemy’s `update` Method

Another approach is to use SQLAlchemy’s `update` method directly, bypassing Pydantic’s `update` method:

session.query(User).filter_by(id=1).update({'name': 'Jane Doe'}, synchronize_session=False)

This method provides more fine-grained control over the update process, allowing us to specify exactly which fields should be updated.

Solution	Pros	Cons
expire_on_commit=False	Easy to implement	Increased memory usage, potential data inconsistencies
exclude_unset	Elegant solution, easy to implement	None
SQLAlchemy’s update method	Fine-grained control, flexible	More complex to implement, requires SQLAlchemy expertise

Best Practices and Conclusion

In conclusion, Pydantic’s behavior of forcing SQL queries to already populated fields can be unexpected, but it’s not a bug – it’s a design choice. By understanding the underlying mechanics and employing one of the proposed solutions, you can avoid this issue and ensure your Pydantic models play nicely with SQLAlchemy.

• Use `exclude_unset` when creating new instances of Pydantic models to avoid unwanted updates.
• Consider using SQLAlchemy’s `update` method for fine-grained control over the update process.
• Avoid using `expire_on_commit=False` unless absolutely necessary, as it can lead to increased memory usage and data inconsistencies.

By following these best practices, you’ll be well-equipped to handle Pydantic’s surprising behavior and build robust, efficient, and scalable applications using Python, Pydantic, and SQLAlchemy.

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